Nowadays, host based solutions are available that enable virtual desktop infrastructure (VDI) deployments capable of providing a robust platform for enterprises hosting virtual desktops from virtual management software (VMS). VDI deployments can scale up by increasing back-end computing power and by adding storage space for desktop consolidation. Storage space may preserve virtual machine (VM) images which load to memory of host computing systems to make the desktops live. A typical performance bottleneck is an input/output (I/O) request issued from a VM to an underlying storage for accessing contents from the VM images. Existing techniques leveraged in the VDI by configuring content based read cache (CBRC) in each host computing system to address such bottleneck issues (e.g., boot storms) when multiple VMs are powered on simultaneously.
However, the CBRC is a per host random access memory (RAM) based solution in the VDT. One limitation with such host specific solutions may be that it can be leveraged only within a single host computing system. However, virtual desktop environments are, typically, deployed on a cluster including multiple host computing systems. In such a virtual computing environment, the above limitation may become crucial. For example, in some implementations, the CBRC is limited to a maximum of 2 gigabytes (GB). During operation, once cached contents reach the maximum limit, the CBRC may remove existing cached contents to accommodate newer cache requirements. It can be envisioned that, during operation, the removed cached contents may be required again by one or more of VMs running on the host computing systems, which can result in unnecessary overhead as data needs to be fetched back into the cache. Further, the 2 GB limitation for the CBRC may become inadequate in the above host specific solution. Increasing the 2 GB limitation may not be a feasible solution view of other memory requirements. In addition, typically, in a virtual desktop environment, VMs are spanned across multiple host computing systems in a cluster, and similar contents are cached across all host computing systems. This can result in significant content cache duplication in the cluster.